When Two AI Features Compete for the Same Click
A user lands on a search results page. Team A's smart summary fires in the top banner: "Here's the gist — skip the list." Team B's inline assistant pulses on the side: "Stay here, I'll keep reading with you." Both prompts compete for the same 800ms of attention, and the user — annoyed — closes the tab. The next morning, Team A reports a 6% lift in summary clicks; Team B reports a 4% lift in assistant opens; nobody in the room is wrong, and the product is worse than it was a quarter ago.
This is the failure mode that the standard playbook of independent feature teams and per-feature A/B tests cannot see. Each team locally optimized against its own metric. The user — who only has one attention budget, one mental model, and one click to give — paid the bill for the integration both teams declined to do.
